
import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowercase    :   Tuple						=			logging.getLogger(__name__)

lowercase    :   Optional[int]						=			tf.data.AUTOTUNE




def       _SCREAMING_SNAKE_CASE   (						)   ->       Tuple:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   argparse.ArgumentParser(description="Train a masked language model on TPU.")
							parser.add_argument(
							    "--pretrained_model_config"		,    type=_lowerCamelCase		,    default="roberta-base"		,    help="The model config to use. Note that we don't copy the model's weights, only the config!"		,    )
							parser.add_argument(
							    "--tokenizer"		,    type=_lowerCamelCase		,    default="unigram-tokenizer-wikitext"		,    help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."		,    )

							parser.add_argument(
							    "--per_replica_batch_size"		,    type=_lowerCamelCase		,    default=8		,    help="Batch size per TPU core."		,    )

							parser.add_argument(
							    "--no_tpu"		,    action="store_true"		,    help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."		,    )

							parser.add_argument(
							    "--tpu_name"		,    type=_lowerCamelCase		,    help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."		,    default="local"		,    )

							parser.add_argument(
							    "--tpu_zone"		,    type=_lowerCamelCase		,    help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."		,    )

							parser.add_argument(
							    "--gcp_project"		,    type=_lowerCamelCase		,    help="Google cloud project name. Only used for non-Colab TPU nodes.")

							parser.add_argument(
							    "--bfloat16"		,    action="store_true"		,    help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."		,    )

							parser.add_argument(
							    "--train_dataset"		,    type=_lowerCamelCase		,    help="Path to training dataset to load. If the path begins with `gs://`"
							    " then the dataset will be loaded from a Google Cloud Storage bucket."		,    )

							parser.add_argument(
							    "--shuffle_buffer_size"		,    type=_lowerCamelCase		,    default=2**18		,    help="Size of the shuffle buffer (in samples)"		,    )

							parser.add_argument(
							    "--eval_dataset"		,    type=_lowerCamelCase		,    help="Path to evaluation dataset to load. If the path begins with `gs://`"
							    " then the dataset will be loaded from a Google Cloud Storage bucket."		,    )

							parser.add_argument(
							    "--num_epochs"		,    type=_lowerCamelCase		,    default=1		,    help="Number of epochs to train for."		,    )

							parser.add_argument(
							    "--learning_rate"		,    type=_lowerCamelCase		,    default=1e-4		,    help="Learning rate to use for training."		,    )

							parser.add_argument(
							    "--weight_decay_rate"		,    type=_lowerCamelCase		,    default=1e-3		,    help="Weight decay rate to use for training."		,    )

							parser.add_argument(
							    "--max_length"		,    type=_lowerCamelCase		,    default=512		,    help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"		,    )

							parser.add_argument(
							    "--mlm_probability"		,    type=_lowerCamelCase		,    default=0.1_5		,    help="Fraction of tokens to mask during training."		,    )

							parser.add_argument("--output_dir"		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help="Path to save model checkpoints to.")
							parser.add_argument("--hub_model_id"		,    type=_lowerCamelCase		,    help="Model ID to upload to on the Hugging Face Hub.")

							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()
							return args




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       Optional[int]:






							'''simple docstring'''





							try:
														if args.tpu_name:
																					__UpperCamelCase      :  str       =   tf.distribute.cluster_resolver.TPUClusterResolver(
																					    args.tpu_name		,    zone=args.tpu_zone		,    project=args.gcp_project)
														else:
																					__UpperCamelCase      :  List[str]       =   tf.distribute.cluster_resolver.TPUClusterResolver()
							except ValueError:
														raise RuntimeError(
														    "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
														    "--gcp_project. When running on a TPU VM, use --tpu_name local.")

							tf.config.experimental_connect_to_cluster(_lowerCamelCase)
							tf.tpu.experimental.initialize_tpu_system(_lowerCamelCase)

							return tpu




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int])   ->       List[str]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   0
							for file in file_list:
														__UpperCamelCase      :  Optional[Any]       =   file.split("/")[-1]
														__UpperCamelCase      :  Optional[Any]       =   re.search(R"-\d+-(\d+)\.tfrecord"		,    _lowerCamelCase).group(1)
														__UpperCamelCase      :  int       =   int(_lowerCamelCase)
														num_samples += sample_count

							return num_samples




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Any		,    _lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     Dict		,    _lowerCamelCase					:     int		,    _lowerCamelCase					:     Optional[int]=None)   ->       Any:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[int]       =   count_samples(_lowerCamelCase)
							__UpperCamelCase      :  Tuple       =   tf.data.Dataset.from_tensor_slices(_lowerCamelCase)
							if shuffle:
														__UpperCamelCase      :  Union[str, Any]       =   dataset.shuffle(len(_lowerCamelCase))
							__UpperCamelCase      :  int       =   tf.data.TFRecordDataset(_lowerCamelCase		,    num_parallel_reads=_lowerCamelCase)
							# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
							__UpperCamelCase      :  List[Any]       =   dataset.apply(tf.data.experimental.assert_cardinality(_lowerCamelCase))
							__UpperCamelCase      :  List[str]       =   dataset.map(_lowerCamelCase		,    num_parallel_calls=_lowerCamelCase)
							if shuffle:
														assert shuffle_buffer_size is not None
														__UpperCamelCase      :  str       =   dataset.shuffle(args.shuffle_buffer_size)
							__UpperCamelCase      :  Dict       =   dataset.batch(_lowerCamelCase		,    drop_remainder=_lowerCamelCase)
							__UpperCamelCase      :  Any       =   dataset.map(_lowerCamelCase		,    num_parallel_calls=_lowerCamelCase)
							__UpperCamelCase      :  Dict       =   dataset.prefetch(_lowerCamelCase)
							return dataset




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any])   ->       Optional[Any]:






							'''simple docstring'''





							if not args.no_tpu:
														__UpperCamelCase      :  Any       =   initialize_tpu(_lowerCamelCase)
														__UpperCamelCase      :  Any       =   tf.distribute.TPUStrategy(_lowerCamelCase)
							else:
														__UpperCamelCase      :  Any       =   tf.distribute.OneDeviceStrategy(device="/gpu:0")

							if args.bfloataa:
														tf.keras.mixed_precision.set_global_policy("mixed_bfloat16")

							__UpperCamelCase      :  Optional[int]       =   AutoTokenizer.from_pretrained(args.tokenizer)
							__UpperCamelCase      :  List[str]       =   AutoConfig.from_pretrained(args.pretrained_model_config)
							__UpperCamelCase      :  Optional[Any]       =   tokenizer.vocab_size

							__UpperCamelCase      :  Optional[int]       =   tf.io.gfile.glob(os.path.join(args.train_dataset		,    "*.tfrecord"))
							if not training_records:
														raise ValueError(F'No .tfrecord files found in {args.train_dataset}.')
							__UpperCamelCase      :  Tuple       =   tf.io.gfile.glob(os.path.join(args.eval_dataset		,    "*.tfrecord"))
							if not eval_records:
														raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.')

							__UpperCamelCase      :  int       =   count_samples(_lowerCamelCase)

							__UpperCamelCase      :  Any       =   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
							__UpperCamelCase      :  List[str]       =   steps_per_epoch * args.num_epochs

							with strategy.scope():
														__UpperCamelCase      :  Union[str, Any]       =   TFAutoModelForMaskedLM.from_config(_lowerCamelCase)
														model(model.dummy_inputs)  # Pass some dummy inputs through the model to ensure all the weights are built
														__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   create_optimizer(
														    num_train_steps=_lowerCamelCase		,    num_warmup_steps=total_train_steps // 20		,    init_lr=args.learning_rate		,    weight_decay_rate=args.weight_decay_rate		,    )

														# Transformers models compute the right loss for their task by default when labels are passed, and will
														# use this for training unless you specify your own loss function in compile().
														model.compile(optimizer=_lowerCamelCase		,    metrics=["accuracy"])

							def decode_fn(_lowerCamelCase					:     List[str]):
														__UpperCamelCase      :  List[Any]       =   {
														    "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa		,    shape=(args.max_length,)),
														    "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa		,    shape=(args.max_length,)),
														}
														return tf.io.parse_single_example(_lowerCamelCase		,    _lowerCamelCase)

							# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
							# use their methods in our data pipeline.
							__UpperCamelCase      :  Optional[Any]       =   DataCollatorForLanguageModeling(
							    tokenizer=_lowerCamelCase		,    mlm_probability=args.mlm_probability		,    mlm=_lowerCamelCase		,    return_tensors="tf")

							def mask_with_collator(_lowerCamelCase					:     int):
														# TF really needs an isin() function
														__UpperCamelCase      :  str       =   (
														    ~tf.cast(batch["attention_mask"]		,    tf.bool)
														    | (batch["input_ids"] == tokenizer.cls_token_id)
														    | (batch["input_ids"] == tokenizer.sep_token_id)
														)
														__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   data_collator.tf_mask_tokens(
														    batch["input_ids"]		,    vocab_size=len(_lowerCamelCase)		,    mask_token_id=tokenizer.mask_token_id		,    special_tokens_mask=_lowerCamelCase		,    )
														return batch

							__UpperCamelCase      :  Dict       =   args.per_replica_batch_size * strategy.num_replicas_in_sync

							__UpperCamelCase      :  Dict       =   prepare_dataset(
							    _lowerCamelCase		,    decode_fn=_lowerCamelCase		,    mask_fn=_lowerCamelCase		,    batch_size=_lowerCamelCase		,    shuffle=_lowerCamelCase		,    shuffle_buffer_size=args.shuffle_buffer_size		,    )

							__UpperCamelCase      :  List[Any]       =   prepare_dataset(
							    _lowerCamelCase		,    decode_fn=_lowerCamelCase		,    mask_fn=_lowerCamelCase		,    batch_size=_lowerCamelCase		,    shuffle=_lowerCamelCase		,    )

							__UpperCamelCase      :  List[Any]       =   []
							if args.hub_model_id:
														callbacks.append(
														    PushToHubCallback(output_dir=args.output_dir		,    hub_model_id=args.hub_model_id		,    tokenizer=_lowerCamelCase))

							model.fit(
							    _lowerCamelCase		,    validation_data=_lowerCamelCase		,    epochs=args.num_epochs		,    callbacks=_lowerCamelCase		,    )

							model.save_pretrained(args.output_dir)


if __name__ == "__main__":
		lowercase    :   Tuple						=			parse_args()
		main(args)

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)